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Clim. Past, 9, 1571–1587, 2013
www.clim-past.net/9/1571/2013/
doi:10.5194/cp-9-1571-2013
© Author(s) 2013. CC Attribution 3.0 License.
Methods and
Data Systems
R. O’ishi1,2 and A. Abe-Ouchi1,3
1 Atmosphere
and Ocean Research Institute, University of Tokyo, Kashiwa, Japan
Institute of Polar Research, Tokyo, Japan
3 Frontier Research Center for Global Change, Yokohama, Japan
Open Access
Influence of dynamic vegetation on climate change and terrestrial
Geoscientific
carbon storage in the Last Glacial Maximum
Instrumentation
2 National
Received: 5 November 2012 – Published in Clim. Past Discuss.: 27 November 2012
Revised: 10 May 2013 – Accepted: 3 June 2013 – Published: 22 July 2013
Open Access
Hydrology and
etation feedback causes substantial
terrestrial carbon storage
Earth System
change, especially in explaining the lowering of atmospheric
Sciences
CO2 during the LGM.
1
Introduction
Ocean Science
Open Access
Paleoenvironmental reconstructions indicate a cold period
around 21 000 yr ago, known as the Last Glacial Maximum
(LGM), which is characterized by the huge extension of an
ice sheet and lower atmospheric CO2 concentration. Reconstruction indicates that two huge
ice sheets
covered the northSolid
Earth
ern part of the North American continent and northern Europe (Peltier, 1994, 2004). An equivalent amount of sea water is removed from the LGM ocean, so that sea level during
the LGM is lower by 120 to 150 m than that of the presentday (Yokoyama et al., 2000). Paleo-ocean reconstruction
shows about 2 to 3 ◦The
C cooling
of the sea-surface temperaCryosphere
ture compared to the present-day in the tropical ocean and
more than 10 ◦ C cooling in high latitudes (CLIMAP Project
Members, 1976, 1984; Kucera et al., 2005; Waelbroeck et al.,
2009). Pollen-based reconstruction also indicates more than
10 ◦ C cooling of ice-free land in North America and Europe
(Bartlein et al., 2011). A cooler climate reduces precipitation,
thus the land surface is drier than at present-day (Kohfeld
and Harrison, 2000; Yu et al., 2000). Reconstructed information from ice-core components indicates that the atmospheric CO2 concentration during the LGM is about 185 ppm
(Jouzel et al., 1993; Monnin et al., 2001), which is 100 ppm
lower than it is in preindustrial times (285 ppm). Due to
these cool, dry, and low-CO2 conditions, the vegetation
Open Access
Published by Copernicus Publications on behalf of the European Geosciences Union.
Open Access
Abstract. When the climate is reconstructed from paleoevidence, it shows that the Last Glacial Maximum (LGM, ca.
21 000 yr ago) is cold and dry compared to the present-day.
Reconstruction also shows that compared to today, the vegetation of the LGM is less active and the distribution of vegetation was drastically different, due to cold temperature,
dryness, and a lower level of atmospheric CO2 concentration (185 ppm compared to a preindustrial level of 285 ppm).
In the present paper, we investigate the influence of vegetation change on the climate of the LGM by using a coupled atmosphere-ocean-vegetation general circulation model
(AOVGCM, the MIROC-LPJ). The MIROC-LPJ is different
from earlier studies in the introduction of a bias correction
method in individual running GCM experiments. We examined four GCM experiments (LGM and preindustrial, with
and without vegetation feedback) and quantified the strength
of the vegetation feedback during the LGM. The result shows
that global-averaged cooling during the LGM is amplified
by +13.5 % due to the introduction of vegetation feedback.
This is mainly caused by the increase of land surface albedo
due to the expansion of tundra in northern high latitudes
and the desertification in northern middle latitudes around
30◦ N to 60◦ N. We also investigated how this change in climate affected the total terrestrial carbon storage by using offline Lund-Potsdam-Jena dynamic global vegetation model
(LPJ-DGVM). Our result shows that the total terrestrial carbon storage was reduced by 597 PgC during the LGM, which
corresponds to the emission of 282 ppm atmospheric CO2 . In
the LGM experiments, the global carbon distribution is generally the same whether the vegetation feedback to the atmosphere is included or not. However, the inclusion of veg-
Open Access
Geoscientific
Model Development
Correspondence to: R. O’ishi ([email protected])
M
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R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
distribution during the LGM is different from that of today
in that it included an expanded arid area, a southward shift of
the boreal forest belt, and a southward expansion of tundra
(Prentice and Webb, 1998; Prentice and Jolly, 2000; Prentice
and Harrison, 2009).
The paleoclimate modeling community tried to reproduce
the LGM climate by using atmosphere general circulation
models (AGCMs) and coupled atmosphere-ocean general
circulation models (AOGCMs) in the Paleoclimate Modeling Intercomparison Project (PMIP). The results of the PMIP
show that general circulation models (GCMs) are generally
able to reproduce consistent cooling (Joussaume and Taylor,
2000; Braconnot et al., 2007). The paleovegetation distribution during the LGM is also investigated, using vegetation
models with GCM results as input. This predicted qualitatively consistent vegetation changes with climate change in
the LGM (Harrison and Prentice, 2003; Prentice et al., 2011).
To introduce vegetation-climate feedback into GCMs, coupled atmosphere-ocean-vegetation GCMs (AOVGCMs) are
developed. By using the AOVGCMs for the LGM experiments, these models well reproduce both climate and vegetation change in the LGM compared to the present-day
(Wyputta and McAvaney, 2001; Ganopolski, 2003; Crucifix
et al., 2005a; Crucifix et al., 2005b; Crucifix and Hewitt,
2005; Jahn et al., 2005; Henrot et al., 2009). These results
reveal how changes in the land surface during the LGM
contributes to the climate through changes of surface heat
and water balance. Dust emission change due to vegetation
change is also noted as an important factor of the vegetation
during the LGM (Mahowald et al., 2006; Takemura et al.,
2009).
Another aspect of vegetation change during the LGM is
the change in the carbon cycle that affects atmospheric CO2
concentration. Not only the distribution of vegetation but also
the strength of photosynthesis and soil carbon decomposition are important factors in terrestrial carbon distribution
(Gerber et al., 2004; Prentice and Harrison, 2009). As far as
the terrestrial carbon storage, reconstruction indicates a 300
to 700 PgC reduction during the LGM (Bird et al., 1994),
and estimation by LGM simulation with GCMs shows almost the same range of terrestrial carbon reduction (Prentice
et al., 1993; Friedlingstein et al., 1995; François et al., 1998,
1999; Kaplan et al., 2002; Otto et al., 2002; Köhler and
Fischer, 2004; Köhler et al., 2005; Ciais et al., 2011). For
this kind of prediction of past terrestrial carbon storage including LGM, it is usual to use a dynamic global vegetation
model (Joos et al., 2004).The uncertainty around the amount
of terrestrial carbon stored during the LGM is thus larger
than 200 PgC, which is equivalent to a reduction in the atmosphere of 100 ppm CO2 (Jouzel et al., 1993; Monnin et al.,
2001). If we consider the sea level change during the LGM,
about 200 PgC could have been stored in the exposed continental shelf (Faure et al., 1996; Zeng, 2003; Montenegro
et al., 2006), and the uncertainty in this may be comparable
to that of the change in atmospheric CO2 .
Clim. Past, 9, 1571–1587, 2013
As shown above, there is uncertainty in the amount of
change in terrestrial carbon, but it is important for understanding the LGM environment, especially the change in
atmospheric CO2 . In the present study, we newly apply a
bias correction procedure in a running AOVGCM to reduce
unrealistic vegetation feedback caused by temperature and
precipitation bias. Then we investigate the climate of the
LGM, the vegetation feedback onto the climate and the resultant equilibrium carbon storage with this newly constructed
bias-corrected AOVGCM in order to quantify vegetationatmosphere feedback in the LGM and also quantify which
aspect of the LGM influences the terrestrial carbon storage
in the LGM. To save integration time, we actually use a slabocean instead of a full ocean general circulation model as
a component of our AOVGCM. We also run several offline
vegetation model experiments to obtain the equilibrium carbon storage which corresponds to climate of these AOVGCM
experiments as input to an offline experiment. In the present
study, we separate difference of terrestrial carbon storage into
three kinds: response to the LGM climate, washout by icesheet coverage, and the new stock on the exposed continental shelf. We also apply the AOGCM for the LGM in order
to evaluate the vegetation-climate feedback during the LGM
and to use the equilibrium climate as input to the vegetation
module, and also to estimate how vegetation evaluation after
the AOGCM differs from the result that was consistent with
those of the AOVGCM.
2
2.1
Model description
Model components
In the present study, a coupled vegetation general circulation model MIROC-LPJ (O’ishi and Abe-Ouchi, 2011)
is used. The atmosphere component of the MIROC-LPJ
is CCSR/NIES/FRCGC AGCM (Center for Climate System Research/National Institute for Environmental Studies/Frontier Research Center for Global Change atmospheric
general circulation model), which is the same as the atmospheric part of an atmosphere-ocean coupled GCM known
as the Model for Interdisciplinary Research on Climate
(MIROC) (Hasumi and Emori, 2004) that contributed to the
fourth assessment report of the Intergovernmental Panel on
Climate Change (IPCC AR4)(Meehl et al., 2007).
The dynamical vegetation component is the LundPotsdam-Jena Dynamic Global Vegetation Model (LPJDGVM) (Sitch et al., 2003), which predicts the distribution
of ten vegetation types (plant functional types, PFTs) based
on the bioclimatic limits, photosynthesis, respiration, carbon
allocation, plant establishment, growth, turnover, mortality,
and competition among the PFTs. In the MIROC-LPJ, an interactive coupling between the atmosphere and the change in
land vegetation is introduced as follows: the monthly mean
surface temperature, precipitation, and cloud cover predicted
www.clim-past.net/9/1571/2013/
R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
by the atmosphere component are used as the input to the
LPJ-DGVM. The terrestrial heat and energy component of
MIROC is Minimal Advanced Treatments of Surface Interaction and RunOff (MATSIRO; Takata et al., 2003), which
does not include a dynamical vegetation component. In this
version of MIROC, fractional coverage of different vegetation types is not yet included and just one vegetation type
represents one land grid cell. Hence we coupled MIROC and
LPJ-DGVM in a very simple way. LPJ-DGVM only predicts
representative vegetation type (which is classified in MATSIRO) for every grid cell annually. Since LPJ-DGVM is able
to predict the distribution of ten PFT types, we translate this
combination of PFTs into MATSIRO vegetation classification with some additional information (leaf area index (LAI),
soil moisture, growing degree days (GDD), net primary productivity (NPP); see O’ishi and Abe-Ouchi, 2009, for detail).
This translated vegetation distribution does not include exact information from LPJ-DGVM but substantial large-scale
characteristics of vegetation distribution. Since LPJ-DGVM
does not handle human-induced change (e.g crops), all “vegetation” in the present study is inevitably potential vegetation. Then MATSIRO solves land surface heat and energy
budget and balance equations according to the vegetation
type (translated from LPJ-DGVM) and corresponding parameters which are based on MATSIRO vegetation classification. We simplified LAI seasonality as a simple sine curve
which is also based on MATSIRO vegetation classification.
Thus in MIROC-LPJ, MATSIRO is able to work if a vegetation type is given in a grid cell whether it is from LPJ-DGVM
or not. We can also choose a specific prescribed vegetation
distribution instead of coupling LPJ-DGVM (this kind of experiment is noted as fixed vegetation in Sect. 3.1).
The original MIROC includes a full ocean dynamical
component COCO (Hasumi, 2006), but it will take too long
a numerical integration to equilibrate because atmosphereocean-vegetation interactive system may have several hundred years (perhaps 1000 yr or more) of time scale to achieve
the equilibrium. In the present study, we actually choose a
slab-ocean model for an ocean component of MIROC-LPJ
instead of the COCO to save integration time. We assume
seasonality of ocean heat transport to predict distribution of
sea surface temperature and sea-ice.
In all experiments, the horizontal and vertical resolutions of the atmospheric correspond to T42 (2.8 × 2.8◦
longitude × latitude) and 20 layers, respectively. Horizontal
resolution of slab-ocean component is also T42.
2.2
Bias correction
The MIROC generally reproduces the climate of today and
the 20th century, but there is an unavoidable temperature and
precipitation bias in the MIROC compared to the observations as a benchmark (The European Centre for MediumRange Weather Forecasts (ECMWF) ERA40 temperature
(Uppala et al., 2005) and the CPC Merged Analysis of Prewww.clim-past.net/9/1571/2013/
1573
cipitation (Xie and Arkin, 1997)). This bias can cause an
incorrect distribution of vegetation in the preindustrial and
present-day, and thus can cause a unrealistic response of the
atmosphere to the change of vegetation distribution in the
paleo-setting experiments. Hence we introduce a bias correction into the MIROC-LPJ. The monthly temperature and
precipitation from the AGCM are modified by using a reference model experiment and observed data, in order to remove
the bias before these variables are passed to the LPJ-DGVM
as follows:
Tinput = Tmodel − (TPD,model − Tobs )
Pinput = Pmodel ·
Pobs
PPD,model
(1)
(2)
where Tmodel and Pmodel are surface air temperature and precipitation predicted in an MIROC-LPJ experiment. Tobs and
Pobs are present-day observational surface air temperature
and precipitation which correspond to ECMWF ERA40 and
CMAP, respectively). TPD,model and PPD,model are surface air
temperature and precipitation, respectively, predicted in the
present-day MIROC-LPJ experiment which adopts an atmospheric CO2 concentration of 345 ppm without this bias correction. Hereafter we can correct temperature and precipitation bias in a running GCM experiment. There is still discussion whether to apply the bias correction and how it would
be done (Hagemann et al., 2011; Ehret et al., 2012). In the
present study, we apply bias correction on monthly mean
temperature and precipitation when LPJ-DGVM predicts
vegetation distribution since the coupling between MIROC
and LPJ-DGVM is very simple, as shown in the previous subsection. There is no problem as far as conservation of energy and water in the land surface process of the
MIROC-LPJ. On the other hand, there can be an uncertainty
caused by the introduction of bias correction. We assume that
model bias in the present-day is as same as preindustrial,
LGM, future,. . . etc. This assumption is not exactly guaranteed because we do not know the actual temperature and precipitation in these other periods. Under other climatic conditions, the model bias may differ from that of the presentday. However, we consider this assumption as a good firstorder approximation to evaluate more correct responses of
atmosphere to vegetation change. Here we show some examples. Since MIROC-LPJ has a warm bias over the land,
the boreal forest/tundra boundary in the control preindustrial
experiment is located further north compared to the actual
boundary if we do not apply bias correction (see Fig. A1a
in the Appendix). This underestimation of tundra in the
control preindustrial experiment may cause weaker climatevegetation feedback in a warm climate. O’ishi and AbeOuchi (2011) show the bias-corrected vegetation change in
the mid-Holocene is able to explain reconstructed warming
in northern high latitude at that time. The southern boundary
of boreal forest is also located farther north and an unrealistic
Clim. Past, 9, 1571–1587, 2013
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R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
gap (covered with grass) appears between boreal and temperate in the control preindustrial experiment if we do not apply
bias correction (Fig. A1a). This unrealistic gap will expand
northward if we try to carry out various kinds of warming
simulations and cause more discrepancy between the control
and warming experiments. When we try to run the LGM simulation (Fig. A1b), there is a northward shift of boreal forest
in Siberia in spite of the cool climate. This strange result is
considered to be induced by the combined effect of a warm
bias, decrease of snow cover and a warming due to the stationary wave effect of the Scandinavian ice sheet (Abe-Ouchi
et al., 2007).
2.3
Carbon balance
The LPJ-DGVM (Sitch et al., 2003) is able to predict four
types of biomass storage (leaf, heartwood, sapwood, and
root) for every PFT, carbon pools that correspond to different
time scales of decomposition, in the coupled MIROC-LPJ.
At first, the net primary productivity (NPP) is calculated
by the gross primary productivity, autotrophic respiration
and growth cost of production. Then NPP is distributed to
four biomass carbon pools shown above. Biomass is moved
to soil carbon pool by turnover, fire event, heat stress and
change of bioclimatic limits. The soil carbon is separated
into three pools with different time scale of decomposition.
The decomposition of soil carbon depends on temperature,
soil moisture and time scale of decomposition. LPJ-DGVM
employs the relation between temperature and soil carbon
decomposition from Lloyd and Taylor (1994).
Different from full carbon coupled GCM experiments
(e.g. Faloon et al., 2012), feedback through carbon balance
is not introduced in the MIROC-LPJ and both the GCM
and LPJ-DGVM are forced by a given level of atmospheric
CO2 . This is because we intend to evaluate the direct effect of vegetation change as land surface boundary condition through water and heat balance upon the climate. Prediction of the equilibrium terrestrial carbon is completed by
an offline LPJ-DGVM experiment using as input the result
of the MIROC-LPJ equilibrium climate. These offline experiments are conducted because it takes many integration
years to equilibrate the terrestrial carbon storage in a coupled MIROC-LPJ setup (see Experimental settings, below).
Another reason is the limitation of MIROC itself because
this version of MIROC (and therefore MIROC-LPJ) does not
include ocean carbon cycle.
3
3.1
Experimental settings
Coupled GCM experiments
Two experiments are preformed by using the MIROC-LPJ,
as shown in Table 1. In the preindustrial control experiment, AOV(PI), the preindustrial level of atmospheric CO2
(285 ppm) and present-day orbital parameters are employed.
Clim. Past, 9, 1571–1587, 2013
In the LGM experiment, AOV(LGM), the LGM level of atmospheric CO2 (185 ppm), the LGM orbital parameters, and
ICE-5G ice sheets (Peltier, 2004) are taken from the Paleoclimate Modeling Intercomparison Project 2 (PMIP2) protocol (Braconnot et al., 2007). The expanded LGM land cover
(+23 × 1012 m2 ) is employed by assuming a 150 m sea level
descent which is the largest estimation (Yokoyama et al.,
2000) in order to evaluate the upper limit of the effect of
difference of coastline, especially for vegetation distribution
and carbon storage. Other green house gases (GHGs), CH4 ,
N2 O, CFC and O3 are set to the preindustrial values because
we intend to extract response of climate and vegetation to
difference of atmospheric CO2 . These experiments are integrated over about 400 yr including spin-up, which is long
enough to reach equilibrium for both vegetation distribution
and climate.
An additional set of experiments, AO(PI) and AO(LGM),
are performed to investigate the effect of vegetation change
on climate. In these experiments, the model does not refer
the LPJ-DGVM, and the vegetation map is fixed to the equilibrium state of the AOV(PI) experiment. For this, we first
extract MATSIRO vegetation type which appeared most frequently during the last 50 yr of AOV(PI) in each grid cells.
This vegetation map is considered to be a representative vegetation distribution which is given to MATSIRO as a surface
boundary condition during the last 50 yr of AOV(PI). Then
we run MIROC-LPJ with this vegetation map instead of LPJDGVM’s prediction. As noted in Sect. 2.1, MIROC-LPJ is
able to run without vegetation index from LPJ-DGVM since
vegetation type is used to choose a set of parameters which
is defined for every vegetation type. In AO(LGM), we use
the same coastline and ice-sheet distribution as AOV(LGM).
Thus, in order to prepare a vegetation map in the AO(LGM),
an offline LPJ-DGVM experiment is performed using the
LGM coastline with the AOV(PI) result as input. This map
is the same as the resultant vegetation map of the AOV(PI)
for those land grid cells that are shared by both the preindustrial and the LGM. Results of the last 50 yr in all experiments
are used for the analysis since vegetation distribution and climate are sufficiently equilibrated with 400 yr integration in
both AOV(PI) and AOV(LGM).
3.2
3.2.1
Offline LPJ-DGVM experiments
Sensitivity experiments
We perform six sensitivity experiments, as shown in Table 2,
using the LPJ-DGVM in an offline mode. In order to compare
the impact of CO2 concentration, precipitation, and temperature on vegetation distribution during the LGM (which is
similar method to Jones et al., 2004; Faloon et al., 2011), one
or two factor(s) are set to the LGM value and the rest to the
preindustrial for input to the LPJ-DGVM. Atmospheric CO2
values are as same as those of AOV(LGM) and AOV(PI).
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R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
1575
Table 1. Settings of the experiments performed in this study.
Experiment
CO2 level
Orbit
Vegetation
MIROC-LPJ
offline LPJ-DGVM
AOV(PI)
AOV(LGM)
AO(PI)
AO(LGM)
285 ppm
185 ppm
285 ppm
185 ppm
0ka
LGM
0ka
LGM
dynamic
dynamic
fixed to AOV(PI)
fixed to AOV(PI)
390 yr
400 yr
75 yr
50 yr
1000 yr
1000 yr
–
1000 yr
Table 2. Settings of the offline sensitivity experiments performed
in this study. Precipitation and temperature are taken from the
AOV(LGM) and the AOV(PI), respectively.
Experiment
CO2
Precipitation
Temperature
integration
CP
TC
TP
T
P
C
185 ppm
185 ppm
285 ppm
285 ppm
285 ppm
185 ppm
LGM
PI
LGM
PI
LGM
PI
PI
LGM
LGM
LGM
PI
PI
1000 yr
1000 yr
1000 yr
1000 yr
1000 yr
1000 yr
Temperature and precipitation are taken from the last 50 yr
time series of both AOV(LGM) and AOV(PI) to include
the interannual variability, and used 20 times repeatedly for
1000 yr of integration; the results of the last 100 yr are used
for analysis.
3.2.2
Carbon equilibrium
Since changes in soil carbon have a long time scale, approximately four hundred years of AOVGCM integration in Table 1 is too short to equilibrate the total terrestrial carbon storage. In analogy to the experiments described in Sect. 3.2.1
(Table 2), we performed further 1000 yr-long offline experiments but now using the full forcings derived from our two
AOVGCM experiments. In order to evaluate the equilibrium
carbon storage in the LGM and the preindustrial era, offline
LPJ-DGVM experiments are performed, using the result of
the last 50 yr time series in the two AOVGCM experiments
as input and letting them run for the equivalent of 1000 yr by
giving climate variables 20 times repeatedly. The vegetation
map and the climate are equilibrated far faster than terrestrial carbon storage, so that it is not necessary to integrate
a coupled MIROC-LPJ until carbon storage is equilibrated.
This is the same procedure as was adopted in a previous
study (O’ishi and Abe-Ouchi, 2009). We also examine offline
LPJ-DGVM experiments, using the result of the AO(LGM)
experiment as input. This experiment corresponds to the offline diagnosis of carbon storage using a GCM result without
vegetation feedback as well as Harrison and Prentice (2003).
The last 100 yr of resultant variables are used for analysis.
Carbon storages/fluxes are just averaged over the last 100 yr.
Hence vegetation distribution is predicted by vegetation indices with interannual variability, we choose and show the
www.clim-past.net/9/1571/2013/
most dominant vegetation index during the last 100 yr as a
representative vegetation index.
4
4.1
Results
Vegetation distribution
In the AOV(LGM), there are some typical changes in the vegetation pattern compared to the AOV(PI) (Fig. 1a and b). We
first discuss changes in vegetation as determined according
to the MATSIRO classification. In the south of the Scandinavian and Laurentide ice sheets, boreal forests shift southward and tundra appears. In eastern Siberia, boreal forests
retreat southward and the southern boundary of tundra is relocated to 50 N. The southern boundary of the boreal forests
also shifts southward in North America and China. In the
downstream region of the Scandinavian ice sheet, a slight
northward shift of boreal forest appears. Total reduction of
the area of boreal forest is 14.0 × 1012 m2 and expansion of
tundra is 6.2×1012 m2 . Expansion of desert is seen in Central
Asia, North and South Africa, and North America, totaling
15.1×1012 m2 of increase. In the tropical region, the tropical
forest shrinks, and savanna or grassland appears or increases,
especially in Africa. Tropical forest covers the exposed continental shelf over the maritime continent. The continental
shelf of the East China Sea to south of the Japan Sea is covered by temperate forest. The East Siberian Sea and north of
the Bering Sea turns into tundra. According to LPJ-DGVM
PFT classification, a fraction of grass PFTs in LPJ-DGVM
increase in high latitude (Fig. 1c) and decreases in the central Eurasia. A fraction of forest shows a decrease in both
northern high latitudes and tropical regions (Fig. 1d). The
NPP also shows a drastic change (Fig. 3a and h). The global
total annual NPP during the LGM and the preindustrial are
44.0 PgC yr−1 and 59.5 PgC yr−1 , respectively (Table 3). Reduction of 5.4 PgC yr−1 occurs due to covering by the expanded ice sheet, and an increase of 7.5 PgC yr−1 occurs on
the exposed continental shelf due to a change in sea level
(these values are not shown in the table). In the AOV(LGM),
a large part of the total reduction of NPP is seen in the forest
region (see Fig. 3h) compared to AOV(PI).
By performing additional sensitivity experiments for the
LGM using the offline LPJ-DGVM, but setting one or two
of the forcing parameters to preindustrial values, it is revealed that these vegetation changes are caused by individual
Clim. Past, 9, 1571–1587, 2013
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R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
(a) Vegetation distribution in AOV(PI)
(c) Difference of grass fraction [AOV(LGM) - AOV(PI)]
(b) Vegetation distribution in AOV(LGM)
(d) Difference of forest fraction [AOV(LGM) - AOV(PI)]
Tropical evergreen
Boreal deciduous
Tundra
Tropical deciduous
Temperate mixed
Boreal conifer
Savanna
Ice
-0.4 -0.2 -0.1
0.1 0.2 0.4
Grassland
Desert
Fig. 1. Vegetation distribution obtained in (a) AOV(PI) and (b) AOV(LGM) according to MATSIRO classification. Difference of fractional
Fig. 1. Vegetation distribution obtained in (a) AOV(PI) and (b) AOV(LGM) according to
coverage in (c) grass and (d) forest between AOV(LGM) and AOV(PI) which are from PFT-based variables in the LPJ-DGVM.
MATSIRO classification. Difference of fractional coverage in (c) grass and (d) forest between
AOV(LGM) and AOV(PI) which are from PFT-based variables in the LPJ-DGVM.
Table 3. Equilibrium global NPP (PgC yr−1 ) and global biomass,
soil carbon and total carbon storage (PgC). The sum of four biomass
carbon pools is shown as “biomass”. The sum of three soil carbon
pools is shown as “soil carbon”. Total carbon is sum of biomass and
soil carbon. Values in the parentheses do not include the LGM icesheet area in the preindustrial nor continental shelf area in the LGM
to extract pure response to climate and atmospheric CO2 . Result
of AOV(PI) and upper column of other experiments are absolute
values. In lower columns, values denote absolute differences with
respect to AOV(PI). The last digit can include a rounding error.
Experiment
AOV(PI)
AOV(LGM)
AO(LGM)
CP
TC
TP
T
P
C
NPP
Biomass
Soil carbon
Total
59.5 (54.0)
44.0 (36.5)
−15.5 (−17.6)
44.6 (36.7)
−14.9 (−17.3)
44.2 (36.7)
−15.3 (−17.4)
48.5 (40.2)
−11.0 (−13.8)
57.7 (47.9)
−1.8 (−6.2)
64.1 (53.2)
+4.6 (−0.8)
59.7 (49.5)
+0.2 (−4.5)
49.3 (41.0)
−10.2 (−13.0)
1082 (973)
828 (672)
−254 (−301)
823 (660)
−259 (−313)
808 (653)
−274 (−321)
933 (760)
−149 (−213)
1068 (875)
−14 (−98)
1218 (1001)
+136 (+28)
1047 (856)
−35 (−117)
929 (761)
−153 (−212)
1368 (1088)
1025 (888)
−343 (−200)
1009 (861)
−359 (−227)
853 (765)
−515 (−323)
1207 (1033)
−161 (−55)
1316 (1141)
−52 (+53)
1503 (1298)
+135 (+210)
1101 (984)
−267 (−104)
961 (859)
−407 (−229)
2450 (1062)
1853 (1560)
−597 (−502)
1831 (1522)
−619 (−540)
1661 (1417)
−789 (−645)
2140 (1793)
−310 (−269)
2384 (2016)
−66 (−46)
2721(2299)
+271 (+237)
2148 (1840)
−302 (−222)
1890 (1620)
−560 (−442)
Clim. Past, 9, 1571–1587, 2013
aspects of environmental change during the LGM. These results are compared with AOV(PI) and AOV(LGM) in Fig. 2
according to the MATSIRO classification. Of course an offline experiment is conceptually different from an online experiment, however, the response signals to different temperature, precipitation and CO2 concentration are far larger than
the inconsistency between offline and online. By comparing
the left side of Fig. 2 (a–d, preindustrial temperature) and
36 of Fig. 2 (e–h, LGM temperature), it is shown
the right side
that the temperature changes during the LGM mainly dominated the distribution of forest/tundra boundary. Cooling in
high latitudes shifts the boundary of the forest/tundra to the
south. Around the Scandinavian and Laurentide ice sheets,
tundra appears between the ice sheet and the boreal forests,
due to cooling, in all offline experiments with LGM temperature (Fig. 2e–h). In the tropical regions, cooling compensated for expansion of savanna, which is due to less precipitation and lower CO2 during the LGM but a relatively
higher preindustrial temperature. For example, with LGM
temperature (Fig. 2h), savanna is replaced by forest, compared to correspondent experiments with preindustrial temperature (Fig. 2d). This tendency is also seen in other combinations of precipitation and CO2 (e.g comparison between
Fig. 2a and e, between Fig. 2b and f and between Fig. 2c and
g). This is consistent with NPP changes. In Fig. 3e, reduction of NPP in tropical region is smaller due to decrease of
autotrophic respiration if we apply LGM temperature.
By comparing Fig. 2b, d, f and h (LGM precipitation) and
Fig. 2a, c, e and g (PI precipitation) we see that precipitation
changes during the LGM mainly dominated the distribution
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R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
(a) AOV(PI)
1577
(e) offline exp. T
(b) offline exp. P
(f) offline exp. TP
(c) offline exp. C
(g) offline exp. TC
(d) offline exp. CP
(h) AOV(LGM)
Fig. 2. Vegetation distribution obtained in (a) AOV(PI) and (h) AOV(LGM) (they are as same as Fig. 1 and shown again for convenience).
Fig. 2. Vegetation distribution obtained in (a) AOV(PI) and (h) AOV(LGM) (they are as same as
Vegetation distribution obtained in offline sensitivity experiments (b) P , (c) C, (d) CP, (e) T , (f) TP and (g) TC according to MATSIRO
Figure 1 and shown again for convenience). Vegetation distribution obtained in offline sensitivity
classification.
experiments (b) P, (c) C, (d) CP, (e) T, (f) TP and (g) TC according to MATSIRO classification.
of desert and savanna. Especially the boundary between boreal forests and desert is formed by LGM precipitation. In the
central Eurasia continent, less precipitation during the LGM
expands deserts, especially north and west of the Caspian
Sea, which had the largest decrease in precipitation on the
Eurasian continent. The Gobi and Taklamakan deserts expands slightly northward. The boreal forests of the Pamir
plateau and Tian Shan Mountains turns into desert, due to the
decrease in precipitation. However, the total shift of this forest/desert boundary is not achieved solely by LGM precipitation (Fig. 2b) but additional combined effect with LGM CO2
(Fig. 2d) is also important. The expansion of desert and savanna corresponds to change of NPP. Figure 3b indicates major reduction of NPP in central Eurasia and Sahel in Africa.
www.clim-past.net/9/1571/2013/
Other experiments
with LGM precipitation (Fig. 3d, f and h)
37
show the same pattern of NPP change in these regions.
By comparing Fig. 2c, d, g and h (with LGM CO2 ) and
Fig. 2a, b, e and f (with preindustrial CO2 ), we see that low
CO2 during the LGM mainly dominated the distribution of
savanna in the tropical region e.g. Sahel, South Africa and
Australia (Fig. 2c for example). There is a slight expansion
of desert due to decrease photosynthesis, but the NPP shows
a substantial reduction due to the lower level of CO2 not only
in the tropical region but also in temperate and boreal forests
(Fig. 3c).
These results suggest that the dominant factors for vegetation during the LGM depend on regions. In high latitudes,
low temperature in the LGM shows the major contribution
to the location of boundary between boreal forest tundra.
Clim. Past, 9, 1571–1587, 2013
1578
R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
(a) NPP in AOV(PI)
(e) Relative NPP in offline exp. T
(b) Relative NPP in offline exp. P
(f) Relative NPP in offline exp. TP
(c) Relative NPP in offline exp. C
(g) Relative NPP in offline exp. TC
(d) Relative NPP in offline exp. CP
(h) Relative NPP in AOV(LGM)
Fig. 3. Equilibrium net primary productivity (NPP) [kg m−2 yr−1 ] in (a) AOV(PI) and absolute NPP
with respect to AOV(PI) in
Fig. 3. Equilibrium Net Primary Productivity (NPP) [kg/m2 /yr]differences
in (a) AOV(PI)
and absolute NPP
offline experiments (b) P , (c) C, (d) CP, (e) T , (f) TP and (g) TC. Absolute NPP difference in AOV(LGM) with respect to AOV(PI) is shown
differences
with
respect
to
AOV(PI)
in
offline
experiments
(b)
P,
(c)
C,
(d)
CP,
(e) T, (f) TP and
in (h).
(g) TC. Absolute NPP difference in AOV(LGM) with respect to AOV(PI) is shown in (h).
The other boundary between boreal forest and desert in midlatitudes is most influenced by precipitation decrease. Low
CO2 levels also contribute to vegetation shift but the magnitude is smaller in mid-latitudes. In tropical areas, the lower
atmospheric CO2 during the LGM only changes the vegetation into savanna, and it reduces the NPP, even when the
vegetation distribution is unchanged. There are also synergy
effects of the three different forcing factors. In Amazonia and
Africa, temperate forest replaces tropical forest, so that the
tropical regions shrink toward the equator. This is not only
due to cooling but also to precipitation and CO2 . The southward shift of the tundra/forest boundary in northeast China
is not explained by a single variable. A synergy effect of
temperature, precipitation, and CO2 causes the total response
of vegetation in these regions. However, a total amount of
these synergy effects is limited. The response of NPP, which
explains vegetation change well, indicates that temperature,
Clim. Past, 9, 1571–1587, 2013
38 and CO2 effects are almost additive (Table 3).
precipitation
The sum of individual responses of NPP to temperature (T ,
−0.8 PgC yr−1 ), precipitation (P , −4.5 PgC yr−1 ) and CO2
(C, −13.0 PgC yr−1 ) is very close to that of total AOV(LGM)
responses (−17.6 PgC yr−1 ). A synergy effect is seen between temperature and precipitation by comparing TP result
(−6.2 PgC yr−1 ) with the sum of T and P . Synergy of precipitation and CO2 (CP, −17.4 PgC yr−1 ) shows close value
to that of AOV(LGM). The CO2 effect on NPP seems to be
additive to temperature and/or precipitation effect.
4.2
LGM climate and impact of vegetation change
In the AOV(LGM), the globally averaged 2 m air temperature is decreased by 4.88 K compared to the AOV(PI), due to
orbital change, lowered atmospheric CO2, expansion of the
ice sheets, and changes in vegetation. On the other hand, in
www.clim-past.net/9/1571/2013/
(a) 2m T change between AOV(LGM) and AOV(PI)
(b) Vegetation-induced 2m T change
1579
(c) precipitation change between AOV(LGM) and AOV(PI)
(d) Vegetation-induced precipitation change
Fig. 4. Annual averaged
m temperature
change [K] (the
interval ischange
2 K) and precipitation
change [mm
yr−1 ] (the
interval
is
Fig.24.
Annual averaged
2m contour
temperature
[K] (the contour
interval
is 2contour
K) and
precip100 mm yr−1 ) in the experiment AOV(LGM) from AOV(PI) are shown in panels (a) and (c), respectively. Contribution of vegetation change
itation change [mm/yr] (the contour interval is 100 mm/yr) in the experiment AOV(LGM) from
in panels (b) (2 m temperature, the contour interval is 1 K) and (d) (precipitation, the contour interval is 100 mm yr−1 ) are calculated from
AOV(PI)
are shown
in panels
andshow
c, respectively.
Contribution
vegetation change in panels
(AOV(LGM) − AOV(PI))
- (AO(LGM)
− AO(PI)).
Dottedaareas
95 % significance
by Student’s t of
test.
b (2m temperature, the contour interval is 1 K) and d (precipitation, the contour interval is 100
mm/yr) are calculated from (AOV(LGM) − AOV(PI)) − (AO(LGM) − AO(PI)). Dotted areas
the AO(LGM), globally
averaged
surface airby
temperature
show 95%
significance
student’sist-test.nificant (Fig. 4d), hence snow cover and surface albedo de-
decreased by 4.29 K compared to the AO(PI). Thus change of
vegetation contributes 0.58 K of global cooling, which corresponds to 13.5 % amplification of global cooling compared
AO results. The global distribution of temperature change in
the AOV(LGM) (Fig. 4a) shows a strong cooling over the ice
sheets in North America and northern Europe. In other land
areas in the Northern Hemisphere, cooling is around 4 K to
8 K. Precipitation shows general decrease globally between
AOV(LGM) and AOV(PI). Decrease of precipitation is especially larger in tropical Africa, tropical Pacific Ocean and
northern mid- and high latitude (Fig. 4c).
We extracted glacial temperature change and precipitation change due to vegetation by multiple subtraction as
(AOV(LGM) − AOV(PI)) − (AO(LGM) − AO(PI)). The result indicates that cooling due to vegetation change is mainly
allocated around the middle latitudes of the Northern Hemisphere (Fig. 4b). In Eurasia, more than 30 % (at most 80 %)
of the total cooling at around 45◦ N is due to vegetation
change, which is obtained by dividing the contribution of
vegetation change to temperature change by total temperature change. In North America, except for the ice sheet, a
contribution of vegetation change to the total cooling around
45◦ N exists, however, it is less than 30 %. In this area, a
change of vegetation from forest to tundra or desert increase
the land surface albedo (Fig. 5a) and surface shortwave absorption is reduced (Fig. 6a). This is due to not only vegetation change itself but also snow cover change induced
by vegetation change in specific region (Fig. 5b). Surface
albedo decreases in northern Siberia in spite of less vegetation change, and is seen to explain albedo change. In this
region, precipitation decrease due to vegetation change is sig-
www.clim-past.net/9/1571/2013/
crease. Change of vegetation contributes to the cooling not
only over land but also on the ocean (Fig. 4a). Around 45◦ N,
a southern shift of the sea-ice boundary in winter (Fig. 5c)
increases the surface albedo (Fig. 5a) south of Nova Scotia.
A slight increase in albedo is seen in the northwestern part of
the Pacific Ocean which is related to an increase of sea-ice
coverage as well.
39tropical region, a slight (and non-significant) warmIn the
ing occurs due to vegetation change (Fig. 4b). This is directly
from the increase of sensible heat (Fig. 6c), which is caused
by a reduction in precipitation (Fig. 4d) and thus evaporation
(Fig. 6d). This additional reduction of precipitation shows
similar patterns to that of total precipitation change in the
LGM mainly (Fig. 4c) so that vegetation change amplifies a
decrease of precipitation in the LGM. A significant change
induced by vegetation change is seen in the tropical rain belt
and the mid- to high latitude continental areas in the Northern Hemisphere (Fig. 4d). The ratio of precipitation change
due to vegetation change relative to the precipitation of the
AOV(PI) is most significant in the African Sahel (Fig. 4d).
4.3
Terrestrial carbon storage during the LGM
In the present study, we examined three offline LPJ-DGVM
experiments in order to obtain the equilibrium carbon storage. AOV(LGM), LGM(PI) and AO(LGM) were chosen as
input for the offline LPJ-DGVM (see Table 1 last column).
The sum of four biomass carbon pools and sum of three
soil carbon pools as simulated by the offline LPJ-DGVM
are shown as “biomass” and “soil carbon”, respectively,
in Table 3. The results of the offline LPJ-DGVM experiments are driven with the results from the AOV simulation
Clim. Past, 9, 1571–1587, 2013
scussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |
R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
(a) Vegetation-induced surface albedo change
(c) Vegetation-induced DJF sea-ice cover change
(b) Vegetation-induced DJF snow cover change
(d) Vegetation-induced JJA sea-ice cover change
Fig. 5. (a) AnnuallyFig.
averaged
(which was calculated
as (AOV(LGM)
− AOV(PI))
(AO(LGM)
− AO(PI))
5. contribution
Annuallyof vegetation
averagedchange
contribution
of vegetation
change
(which −was
calculated
as
to surface albedo, (b)
same
as
(a)
but
December-January-February
averaged
snow
cover
fraction,
(c)
same
as
(a)
but
December-January(AOV(LGM) − AOV(PI)) − (AO(LGM) − AO(PI)) to surface albedo, (b) as same as (a) but
February averaged sea-ice cover fraction and (d) same as (a) June-July-August averaged sea-ice cover ratio.
December-January-February averaged snow cover fraction, (c) as same as (a) but DecemberJanuary-February averaged sea-ice cover fraction and (d) as same as (a) June-July-August
averaged
cover
ratio.
storage under or over the ice sheet in the two LGM experito be virtually the
same as sea-ice
the actual
GCM
equilibrium.
ments. We excluded those regions covered by the LGM ice
The AO(LGM) case is used to quantify how lack of the
sheet so as to be consistent with this assumption. In the preinvegetation–atmosphere interaction influences on terrestrial
dustrial experiment our results show that 108 Pg biomass carcarbon storage.
bon out of 1082 Pg, and 280 Pg soil carbon out of 1368 Pg,
The AOV(PI) experiment shows 1092 Pg global biomass
are considered to be “removed” by the LGM ice sheets in
carbon and 1368 Pg global soil carbon, so that the total global
North America and Scandinavia. On the other hand, the concarbon storage is 2450 Pg (Table 3). These values are relatinental shelf is exposed because of a descent in the sea level,
tively larger than previous studies which indicate 500–950 Pg
so that
global biomass carbon, 850–1700 Pg global soil carbon in
40terrestrial carbon can be stored there during the LGM.
the preindustrial (Cramer et al., 2001; Prentice et al., 2011)
In the AOV(LGM), 157 Pg biomass carbon and 137 Pg soil
carbon newly appears on the exposed continental shelf. In
and 2300 Pg total global carbon in the present-day (Denman
the AO(LGM), these values are 162 PgC and 147 PgC, reet al., 2007; Ciais et al., 2011). The AOV(LGM) experiment
spectively. All the values in the two LGM experiments in
shows 828 Pg biomass carbon and 1025 Pg soil carbon, hence
Table 3 include both a reduction due to the ice sheet and
the total global carbon storage is 1853 Pg (Table 3). These
an increase due to the extended continental shelf. In order
values are also relatively larger than previous studies which
to extract the response of the terrestrial carbon pools to the
indicate about 1400 Pg total global carbon (Ciais et al., 2011;
LGM climate, we leave out the areas of the ice sheets and
Prentice et al., 2011). During the LGM, the total terrestrial
continental shelves and listed their values in parentheses in
carbon storage is reduced by 597 Pg compared to that of the
Table 3. In the AOV(LGM), a 301 Pg reduction of biomass
preindustrial era. Reduction of 254 PgC out of 597 PgC is due
carbon and a 200 Pg reduction of soil carbon occur due to
to the change in biomass, and 343 PgC is due to the change
in soil carbon. The total reduction of terrestrial carbon durthe LGM climate and CO2 level. In the AO(LGM), reduction of biomass and soil carbon pools are 313 Pg and 227 Pg,
ing the LGM is equivalent to 282 ppm CO2 emission to the
respectively. Generally, the contribution of the LGM climate
atmosphere (by conversion factor 0.47 (Enting, 1992). This
is not so much different from latest value e.g. 0.48 (Zickand extension of the ice sheet and continental shelf to the
feld et al., 2011). On the other hand, by using the AO(LGM)
total carbon reduction (597 PgC) are reduction of 502 PgC,
result, the reduction of carbon storage during the LGM is
reduction of 388 PgC, and increase of 293 PgC, respectively,
slightly larger than that of the AOV(LGM) result (Table 3).
in the AOV(LGM). In the AO(LGM), they are reduction of
Global biomass and global soil carbon are 823 Pg and 1009
540 PgC, reduction of 388 PgC, and increase of 310 PgC, for
Pg, respectively. Hence reduction of total global carbon stora total decrease of 619 PgC.
age is 619 Pg, which is equivalent to 292 ppm CO2 emission
In areas that do not experience an ice sheet or shelf expoto the atmosphere.
sure, the distribution of terrestrial carbon shows substantial
During the LGM there is no vegetation in the areas covchanges that reflect the changes in distribution of vegetation.
ered by the ice sheets. Assuming the flow of the ice sheet
In the AOV(LGM), the largest decrease of biomass occurs in
completely washes out the soil carbon, there is no carbon
the Siberian forest belt (Fig. 7a). In tropical Africa, there is
Clim. Past, 9, 1571–1587, 2013
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cussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |
1580
R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
1581
(a) Vegetation-induced surface shortwave balance
(c) Vegetation-induced sensible heat balance
(b) Vegetation-induced surface longwave balance
(d) Vegetation-induced latene heat balance
Fig. 6. (a) AnnuallyFig.
averaged
of vegetation
change
(which was calculated
as (AOV(LGM)
− AOV(PI))
− (AO(LGM)
− AO(PI))
6. contribution
Annually
averaged
contribution
of vegetation
change
(which
was calculated
as
to surface shortwave balance [W m−2 ] (downward positive), (b) same as (a) but surface longwave balance [W m−2 ] (upward positive),
2
(AOV(LGM)
−
AOV(PI))
−
(AO(LGM)
−
AO(PI))
to
surface
shortwave
balance
[W/m
]
(down−2
−2
(c) same as (a) but surface sensible heat balance [W m ] (upward positive) and (d) same as (a) but surface latent heat balance [W m ]
2
(upward positive). ward positive), (b) as same as (a) but surface longwave balance [W/m ] (upward positive), (c)
as same as (a) but surface sensible heat balance [W/m2 ] (upward positive) and (d) as same as
(a) Relative biomass in AOV(LGM)
Relative soil carbon in AOV(LGM)
(a) but surface
latent heat balance [W/m2 ] (c)
(upward
positive).
(b) Relative biomass in AO(LGM)
(d) Relative soil carbon in AO(LGM)
41
Fig. 7. Equilibrium Fig.
absolute
of biomass
[kg m−2 ] indifferences
(a) AOV(LGM)ofandbiomass
(b) AO(LGM)
with2respect
to AOV(PI).
Equilibrium
7.differences
Equilibrium
absolute
[kg/m
] in (a)
AOV(LGM)
and (b)
−2
absolute differences of soil carbon [kg m ] in (c) AOV(LGM) and (d) AO(LGM) with respect to AOV(PI). Absolute values are shown in
AO(LGM)
with
respect
to
AOV(PI).
Equilibrium
absolute
differences
of
soil
carbon
[kg/m2 ]
the Appendix Fig. A2.
in (d) AOV(LGM) and (d) AO(LGM) with respect to AOV(PI). Absolute values are shown in
Figure
A2.areas also show a
of soil carbon is due to lowering of decomposition rate of soil
also a decrease ofAppendix
biomass. Other
forest
decrease in biomass which is consistent with the decrease of
NPP (Fig. 3h) , but typically less than 5 kgC m−2 . The largest
decrease of soil carbon is seen in East Siberia and Central
Eurasia (Fig. 7c), which corresponds to the replacement of
boreal forest by tundra and desert, respectively. This can be
explained by the decrease of NPP (Fig. 3h) which also shows
the largest decrease in these areas. In these areas, reduction
of NPP causes less input to soil carbon pools so that equilibrium soil carbon decreases. Increase of soil carbon is seen
in northeast China, northern Siberia and the southern edge of
ice sheets in both Europe and North America. This increase
www.clim-past.net/9/1571/2013/
carbon, which corresponds to carbon output from terrestrial
ecosystem, due to low temperature in the LGM. Other circumstantial evidence is shown in change of NPP (Fig. 3h),
which is regarded as carbon input to terrestrial ecosystem.
Change of soil carbon does not show increase in all these regions. In other regions, the soil carbon shows a slight change,
less than ±5 kgC m−2 .
On the other hand, in the AO(LGM), distribution of terrestrial carbon
42 storage is generally similar to that of the
AOV(LGM) in both vegetation and soil carbon pools (Fig. 7b
and d). Differences in these two LGM experiments are seen
Clim. Past, 9, 1571–1587, 2013
1582
R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
in the edge of the tree line (biomass) and in the wide area of
the Northern Hemisphere continent (soil carbon). These differences reflect the changes in habitable zones (biomass) and
cooling (soil carbon) due to vegetation-climate feedback.
Offline LPJ-DGVM experiments show biomass response
in the LGM (except for new land and preindustrial ice sheet
area) to temperature, precipitation and CO2 (almost additive)
as well as NPP. On the other hand, soil carbon is not additive
(Table 3).
5
5.1
Discussion
Vegetation change and amplification of cooling
In the present study, our results indicate a cooler and drier
climate than is shown in previous studies (Braconnot et al.,
2007; Crucifix and Hewitt, 2005; Henrot et al., 2009). Compared to the latest reconstruction (Bartlein et al., 2011), cooling during the LGM is overestimated in both North America
and Europe. On the other hand, cooling is underestimated
in southern Africa. The pattern of precipitation change is
generally the same as the reconstruction in North America
and Europe, but the intensity of change is underestimated.
Since there is relatively less vegetation in these regions and
not a major part of terrestrial carbon storage, the influence
of climate difference on biomass and soil carbon is limited.
We also obtained similar vegetation changes, such as expansion of tundra, a southward shift of boreal forests, and expansion of desert/arid regions, compared to previous studies (Prentice and Jolly, 2000; Harrison and Prentice, 2003;
Crucifix et al., 2005b; Jahn et al., 2005; Henrot et al., 2009;
Prentice et al., 2011). The resultant vegetation-climate feedback is positive, which has been shown in previous sensitivity studies (Ganopolski, 2003; Crucifix et al., 2005a; Henrot
et al., 2009).
In the present study, the most dominant factor in the
vegetation-climate model results is the increase in land surface albedo (Fig. 5a), which is due not only to the replacement of boreal forest by tundra and desert, but also to the increase in snow cover (Fig. 5b) in mid-latitudes, in spite of the
reduction in precipitation (Fig. 4d). Generally, the cooling
in surface temperature is dominated by reduction of sensible
heat from land (Fig. 6c). This sensible heat balance shows
a very similar pattern to that of surface shortwave balance
(Fig. 6a) which is directly related to surface albedo (Fig. 5a).
We obtained −1.55 W m−2 of clear sky surface shortwave
forcing which is shown as −1.4 W m−2 by Crucifix and Hewitt (2005). Surface longwave balance (Fig. 6b) and surface
latent heat balance (Fig. 6d) do not explain the sensible heat
balance (Fig. 6c) induced by vegetation change. In high latitudes, snow cover shows a slight decrease (Fig. 5b), and thus
decreases the albedo (Fig. 5a) due to the reduction of precipitation (Fig. 4) which reflects weakening of global water
cycle due to cooling induced by vegetation change.
Clim. Past, 9, 1571–1587, 2013
The MIROC-LPJ predicts a boreal forest band in western Siberia that is not seen in previous model studies using
GCMs and vegetation models (Harrison and Prentice, 2003;
Crucifix et al., 2005b; Prentice et al., 2011). In those studies, the boreal forest on the Eurasian continent is separated
into east and west sections, or vanished completely. There
are three possible explanations for this overestimation of boreal forest. First, the MIROC-LPJ shows a weaker cooling in
western Siberia than do the PMIP2 model results (Braconnot
et al., 2007). This weak cooling is not due to the positive
vegetation-climate feedback in AOV(LGM) because the offline LPJ-DGVM run, using the result of the AO(LGM), predicts the same vegetation in this region (not shown). The second reason is related to the lack of fractional coverage representation in the land-surface scheme of the MIROC-LPJ. As
described in Sect. 2.3 and O’ishi and Abe-Ouchi (2009), the
land-surface scheme of the MIROC is able to handle only one
vegetation type in a grid cell, and so it does not handle fractional coverage. We defined the representations of the vegetation types by using combination of individual fractional coverage of PFTs, NPP, LAI, GDD and soil moisture calculated
in the coupled LPJ-DGVM. However, there can be an overestimation of a specific vegetation type which is typically
the most dominant vegetation type. In AOV(LGM) in the
present study, forest coverage in western Siberia was about
0.6, which was less than that of AOV(PI). However, this forest is assumed to occupy a whole grid cell in the land-surface
scheme of the MIROC. Thus this treatment underestimates
both the decrease in forest and the cooling due to vegetation
feedback. If we could introduce the fractional representation
into the land-surface scheme of MIROC, reduction of forest
and increase of tundra cause additional cooling in western
Siberia as well as in northeastern China and central Eurasia. The third reason is model bias of LPJ-DGVM itself. In
AOV(PI), a PFT named “boreal needle-leaved summergreen
trees” is overestimated even if we apply a bias correction.
This tendency of overestimation may also cause overestimation of this PFT, which is the most dominant in the boreal
forest band in AOV(LGM). The improvement of the productivity of boreal needle-leaved summergreen trees in preindustrial or present-day simulations may reduce forest band in the
LGM simulation.
5.2
Carbon storage
In the present study, the tendency of terrestrial carbon reduction of our results is similar to that of previous studies
and falls into the typical range of uncertainty (Prentice et al.,
1993; Friedlingstein et al., 1995; François et al., 1998, 1999;
Kaplan et al., 2002; Otto et al., 2002; Köhler and Fischer,
2004) when using climate variables that include vegetation
feedback. The total 597 Pg reduction of terrestrial carbon
falls into the typical range of uncertainty of terrestrial carbon
reduction (300 to 700 PgC). We separated the total reduction
of terrestrial carbon into three kinds: response to the LGM
www.clim-past.net/9/1571/2013/
R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
climate and CO2 , washout by ice-sheet coverage, and new
stock on the exposed continental shelf.
Response of terrestrial biosphere to the cool, dry, and lowCO2 LGM environment is seen in the reasonable reduction of
the NPP (Fig. 3h), biomass (Fig. 7a) and vegetation coverage
(Fig. 1c and d), as well as in previous studies. The contribution of temperature, precipitation and CO2 level in the LGM
to reduction of global total biomass is almost additive (see
T , C and P in Table 3) as same as that of NPP. On the other
hand, summing contributions of these factors to global total soil carbon does not explain the global total soil carbon
reduction in AOV(LGM). Since the response of biomass is
additive, the response of total dead biomass as input to soil
carbon is also considered to be additive. A possible explanation is non-linear relation of temperature and decomposition speed and soil carbon (Lloyd and Taylor, 1994) and nonlinear dependency of decomposition speed and soil carbon to
soil moisture which is related to temperature and precipitation (Foley, 1995) in LPJ-DGVM.
During the LGM, the assumption of washout by the ice
sheet may overestimate the reduction of terrestrial carbon,
because the carbon flux and storage in the present-day that
are predicted by the LPJ-DGVM tend to be larger than the
typical values (Sitch et al., 2003; Denman et al., 2007) and
those among multi-models (Cramer et al., 2001). The second reason is we do not know how much carbon was stored
below an ice sheet during the LGM. We assumed all carbon
is removed by the ice sheet, which has also been assumed
in previous studies. In this case, carbon washout depends
on how much carbon was stored in the preindustrial control
experiment. On the contrary, the Glacial Burial Hypothesis
(Zeng, 2003, 2007) suggests that 500 PgC was buried under
the LGM ice sheets. This value is comparable to a decrease
of 383 PgC, which we assumed to have been removed by the
ice sheets. However, δ 13 C reconstruction indicates that CO2
rise during the last deglaciation is not from land but from
ocean (Lourantou et al., 2010), which is consistent to our
assumption of washout.
Since we use a sea level change of −150 m, which is
the largest estimation (Yokoyama et al., 2000), the exposed
ice-free continental shelf was maximized. In our model setting, the total area of exposed ice-free continental shelf is
23 × 1012 m2 , which is two times larger than that of previous studies (Zeng, 2003; Montenegro et al., 2006). If we assume a more moderate sea level change, such as that noted
in Montenegro et al. (2006), the increase of terrestrial carbon
(293 PgC) is reduced by about half (which is roughly estimated proportionally to the area of ice-free continental shelf)
and falls into the range of previous studies (112–323 PgC).
The impact of using the AOGCM results instead of the
AOVGCM results is far smaller than the total amount of vegetation, soil, and total carbon storage during the LGM (less
than ±20 PgC out of several 1000 PgC; see Table 3) and does
not change the main result in the present study. However,
these differences are non-negligible if we try to explain the
www.clim-past.net/9/1571/2013/
1583
100 ppm difference of atmospheric CO2 concentration between the LGM and the preindustrial era, which corresponds
to 200 PgC.
6
Conclusions
In the present study we apply a coupled atmosphere-oceanvegetation GCM for predicting the LGM climate and vegetation in order to quantify how the vegetation-climate feedback
affected the LGM climate. Our sensitivity experiments show
tendencies similar to previous paleodata and paleomodeling
studies for changes in vegetation and climate. In the present
study, vegetation changes during the LGM amplify cooling
during the LGM by 13.5%, which is mostly caused by increases of the land surface albedo. We separate the LGM environment changes into three factors: temperature, precipitation, and CO2 . We then investigate the impact of each of
these on the vegetation distribution during the LGM. Offline
sensitivity experiments indicate that temperature and precipitation are dominant in the vegetation distribution during the
LGM. Low levels of CO2 during the LGM affects only the
tropical regions.
We also investigate terrestrial carbon storage during the
LGM by offline dynamical vegetation model experiments.
Temperature, precipitation (predicted by GCM experiment)
and atmospheric CO2 levels of GCM experiments are used
as forcing. The results show that terrestrial carbon storage is
generally the same, both in pattern and quantity, regardless
of whether we include vegetation-climate feedback in the input variables. However, the difference is comparable to carbon quantity, which corresponds to the 100 ppm of CO2 difference between the LGM and the preindustrial era. We determine that inclusion of vegetation-climate feedback should
be taken into account if we wish to explain the lowering of
atmospheric CO2 during the LGM.
We separate the terrestrial carbon difference between the
LGM and preindustrial era into three factors: response to the
LGM climate and CO2 , washout by ice-sheet coverage, and
new stock on the exposed continental shelf during the LGM.
In the present study, the total 597 Pg of reduction of terrestrial carbon is explained by a decrease by 502 Pg due to response to the LGM environment, a decrease by 388 Pg removed by the ice sheet, and an increase by 293 Pg due to
new storage on the exposed continental shelf. The decrease
of carbon storage due to the LGM environment is consistent
with the previous model and data studies. Washout by the ice
sheets depends on how much carbon is stored in these regions
in the preindustrial condition. Our results show similar values to those previous studies, regardless of the assumption
that carbon is washed out of or stored under the ice. Carbon storage on the exposed continental shelf is larger than
that of previous studies because we adopt the largest possible
sea level change, as shown by paleo-reconstructions. Carbon
storage on the exposed continental shelf falls into the range
Clim. Past, 9, 1571–1587, 2013
1584
R. O’ishi and A. Abe-Ouchi: Climate and vegetation/carbon change in the LGM
of variability among previous studies if we assumed the same
area as did previous studies.
Our result in the present study is done by just one model.
Since our model tends to overestimate terrestrial carbon
storage compared to previous studies (Cramer et al., 2001;
Denman et al., 2007; Ciais et al., 2011; Prentice et al., 2011),
reduction of terrestrial carbon in the LGM climate is considerably larger in range of uncertainty (Bird et al., 1994). We
also assumed the largest extent of the continental shelf so
that the new storage on the continental shelf is maximized.
Our model is considered to overestimate response (both positive and negative) of carbon storage in the LGM than previous model studies. Our result indicates that total terrestrial
carbon response in the LGM is calculated by various factors
of terrestrial carbon change (ice sheet washout, new storage
on continental shelf, response of photosynthesis and vegetation feedback to LGM climate). It is not yet clear where the
200 PgC is stored; it corresponds to a decrease of 100 ppm
of atmospheric CO2 during the LGM (Lourantou et al., 2010;
Ciais et al., 2011; Shakun et al., 2012). When we try to determine the global allocation of carbon in the earth system
(atmosphere, ocean and land) during the LGM, we find that
not only the uncertainty of photosynthesis response but also
vegetation-climate feedback and the uncertainty of sea level
change should be taken into account. This is a common important problem among other models as well as our model.
Appendix A
(a) Vegetation index in AOV(PI) w/o bias correction
(b) Vegetation index in AOV(LGM) w/o bias correction
Fig. A1. The same as Fig. 1 but without bias correction (a)
AOV(PI) and (b) AOV(LGM).
(a) Biomass in AOV(PI)
(d) Soil carbon in AOV(PI)
(b) Biomass in AOV(LGM)
(e) Soil carbon in AOV(LGM)
(c) Biomass in AO(LGM)
(f) Soil carbon in AO(LGM)
2
Fig. A2. As same as Figure7 but absolute biomass [kg/m ] in−2
Fig.
A2. The same as Fig. 7 but absolute biomass [kg m (a)] AOV(PI),
in (a) (b) AOV(LGM
(c) AO(LGM) and soil carbon [kg/m2 ] in (d) AOV(PI), (d) AOV(LGM) and AO(LGM).
AOV(PI), (b) AOV(LGM) and (c) AO(LGM) and soil carbon
[kg m−2 ] in (d) AOV(PI), (d) AOV(LGM) and AO(LGM).
44
Acknowledgements. This research is supported by the GRENE
Arctic climate Change Research Project.
Edited by: M. Crucifix
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